000			02128naaaa2200385uu 4500
003			BUT
005			20230330152123.0
006			m o d
007			cr|mn|---annan
008			20230215s2022 xx |||||o ||| eng|| d
020			_a9781839698880
020			_a9781839698873
020			_a9781839698897
040			_aoapen _coapen
041	0		_aeng
072		7	_aUNF _2bicssc
080			_a004.8
100	1		_aTang, Niansheng _4edt
245	1	0	_aData Clustering
260			_bIntechOpen _c2022
300			_a1 electronic resource (126 p.)
490	1		_aArtificial Intelligence _vv.10
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aIn view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.
540			_aCreative Commons _fhttps://creativecommons.org/licenses/by/3.0/ _2cc
546			_aEnglish
650		0	_aИскусственный интеллект _94518
653			_aDatabases
700	1		_aTang, Niansheng _4oth
830			_94528 _aArtificial Intelligence _vv.10
856	4	0	_awww.oapen.org _uhttps://mts.intechopen.com/storage/books/10820/authors_book/authors_book.pdf _70 _zDownload
856	4	0	_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/97038 _70 _zDescription
909			_c4 _dDarya Shvetsova
942			_2udc _cEE
999			_c6256 _d6256